对抗-对抗肌肉系统的状态估计
State Estimation For An Agonistic-Antagonistic Muscle System
AI总结：
本文研究基于Hill肌肉模型的非线性对抗-对抗肌肉系统，提出三种观测器用于估计双肌系统状态变量和激活信号，理论分析证明其收敛性，并通过反步控制器验证其在噪声环境下的可靠性。
摘要：
辅助技术、康复和假肢研究需要理解人机交互，其中人类肌肉特性起关键作用。本文基于Hill肌肉模型研究非线性对抗-对抗肌肉系统，考虑参数不确定性和未知输入，提出高增益观测器、滑模观测器和自适应滑模观测器。理论分析证明三种观测器的收敛性。为便于数值仿真，采用反步控制器使肌肉系统跟踪期望轨迹。数值仿真表明，三种观测器在无噪声和有噪声情况下均能提供可靠估计。所提方案可作为复杂多肌系统估计的框架，可能推动智能适应训练与康复设备及自适应假肢和外骨骼的发展。
英文摘要：
Research on assistive technology, rehabilitation, and prosthesis requires the understanding of human machine interaction, in which human muscular properties play a pivotal role. This paper studies a nonlinear agonistic-antagonistic muscle system based on the Hill muscle model. To investigate the characteristics of the muscle model, the problem of estimating the state variables and activation signals of the dual muscle system is considered. In this work, parameter uncertainty and unknown inputs are taken into account for the estimation problem. Three observers are presented: a high gain observer, a sliding mode observer, and an adaptive sliding mode observer. Theoretical analysis shows the convergence of the three observers. To facilitate numerical simulations, a backstepping controller is employed to drive the muscle system to track a desired trajectory. Numerical simulations reveal that the three observers are comparable and provide reliable estimates in noise free and noisy cases. The proposed schemes may serve as frameworks for estimation of complex multi-muscle systems, which could lead to intelligent exercise machines for adaptive training and rehabilitation, and adaptive prosthetics and exoskeletons.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
